Conventional Neuman computers have exercised their great power in computers configured to sequentially execute specific algorithms and have supported modern scientific technologies. In order to design CPU of such a computer, a complicated electronic circuit has to be produced and progressively optimized by executing its simulation. As its design tool, a CAD system for making a complicated electronic circuit is indispensable.
In recent years, information processing learning from brains, such as neural network model, has been widely researched with a hope of realization as exercising its power in pattern recognition. In case a neural network model is practically applied in form of a device, it is preferable to realize a network such as neural network of a brain in form of a certain system. Following such a plan, experiments are being conducted toward artificially making nerves for living bodies, and their future development is being expected.
Nerve cells of brains individually have complicated tree-like projections to form a fractal structure. Such fractal elements grow while interacting with each other, and make up a complicated brain neural network.
In order to simulate the function of a preferable network, a technique for creating such a brain neural network is indispensable. That is, there is a strong demand for a technique for making a complicated network, which corresponds to a CAD system that has been indispensable for conventional Neuman computers. However, there are no conventional techniques that make up a structure coupling a plurality of fractal elements while controlling their fractal nature.
It is therefore an object of the invention to provide a method for making a fractal structure, which can make a complicated network like a neural network easily in a well-controlled manner.
A further object of the invention is to provide a method for making a fractal structure, which can control the coupling mode among different fractal structures and can make complicated networks with a more variety of structures such as neural networks easily in a well-controlled manner.